Built by engineers.
Run by operators.

Pradeep has spent 25 years building software companies at moments when the industry shifted underneath them — from enterprise Java to cloud-native to, now, agentic AI. His track record is less about following trends than about correctly timing bets on infrastructure that enterprises can't afford to ignore.

He founded GTP with a specific thesis: the biggest constraint on enterprise AI adoption isn't the models, it's the legacy plumbing underneath them. That conviction shaped the company's product portfolio. VectorViewer — which processes over 500 million documents annually — grew out of client engagements where document infrastructure was the hidden bottleneck. PayServ, which compresses PSP integration cycles from months to weeks, was built after watching FinTech teams burn entire sprint cycles on payment certification bureaucracy.

Before GTP, Pradeep held senior engineering and architecture roles across scale-up and enterprise environments, and developed a taste for the scaling problems that stall otherwise well-run organizations. He works in architecture diagrams and P&L spreadsheets in roughly equal measure.

Braj's background sits at an unusual intersection: part operator, part commercial strategist, part builder. He joined GTP as a founding partner after a career building and scaling business units where he owned full P&L — revenue targets, margin discipline, talent, and go-to-market strategy running in parallel.

At PayServ, where he serves as Co-Founder and COO, Braj architected the commercial motion from day one: designing the GTM framework, building the partner ecosystem, and taking the platform from early concept to enterprise production. He brings the same instinct to GTP's services business — structuring engagements so clients see measurable outcomes fast, then building the relationships that sustain them beyond the initial project.

His technical fluency across AWS, Azure, and AI-powered SaaS means he can run a sales conversation and a delivery review on the same day. In practice, that's what P&L ownership at a technical firm actually requires.

Jeevan's 28 years in engineering span a range that most technologists don't cover in a single career: from building and running offshore delivery centers for global services firms to architecting autonomous SaaS platforms that operate without human intervention. The through-line is an obsession with eliminating the friction that slows engineering teams down at scale.

His current focus — embedding GenAI and autonomous agents directly into the software development lifecycle — is a natural extension of work he was doing before "AI-first engineering" became an industry term. He was optimizing delivery margins through automation and restructuring human-agent workflows when the tooling was far cruder than it is today. The philosophy hasn't changed; the capabilities have caught up.

At GTP, Jeevan sets the technical direction across AI engineering, product, and global delivery. He's the principal architect behind the Human + Agent Delivery Pod model — the operational structure that lets GTP execute projects at a pace traditional firms can't match.

Sannutha spent 15 years in enterprise quality engineering at a time when QA was treated as an afterthought — a cost to be minimized rather than infrastructure to invest in. Her argument was always the opposite: that test architecture is a load-bearing wall of software delivery, and that brittle test suites are what turn a three-week release into a three-month one.

At GTP, she leads the Quality Engineering practice and is the engineer behind QualixAI — the platform that generates, maintains, and triages test suites autonomously. The core insight driving QualixAI is one Sannutha developed through years of watching teams spend more hours maintaining tests than writing code: AI handles that maintenance work better, faster, and without accumulating the fatigue that turns test debt into release risk.

Her background in scalable automation frameworks and CI pipeline architecture means she approaches AI-driven testing as an engineering discipline, not a research experiment. Clients leave her engagements with test infrastructure that looks after itself.